Heuristic Learning for Ambient Light Sensor Brightness Adaptation
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Solution Overview
Problem
Current information handling systems with ambient light sensors (ALS) for automatic display brightness adjustment suffer from poor user experience due to fixed static response settings, failing to accommodate individual user preferences for ambient lighting and display brightness, leading to users bypassing this feature.
Innovation Solution
A heuristic learning algorithm is employed to generate and maintain customized response curves for ambient lighting and display brightness, adapting to individual user preferences over time by modifying the display brightness based on user input and ambient light sensor output, ensuring positive slope and minimal change sensitivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a fixed static response curve is used for automatic display brightness control, then the system complexity is reduced and ease of manufacture is improved, but the adaptability to individual user preferences deteriorates
Solution Approach 1:
The response curve is transformed from a fixed static configuration to a dynamic adaptive one. The system continuously learns user preferences by monitoring manual brightness adjustments and automatically updates the response curve parameters, enabling the display brightness control to adapt to individual user needs while maintaining automated operation
Solution Approach 2:
The system performs self-learning and self-adjustment by automatically monitoring user manual brightness adjustments and using this information to refine the response curve. This eliminates the need for manual calibration or user configuration, as the system autonomously improves its performance over time based on observed user behavior
2Adaptability or versatility
If manual brightness adjustment is allowed to override automatic settings, then user preference adaptation is improved, but the device complexity increases due to heuristic learning algorithms
Solution Approach 1:
The system implements a feedback mechanism where manual brightness adjustments by the user are continuously monitored and fed back to the heuristic learning algorithm. This feedback loop enables the system to learn from user behavior and automatically refine the response curve, transforming user overrides from disruptive actions into learning opportunities that improve adaptability without requiring complex additional hardware
Data Source
AI summary
A heuristic learning algorithm uses an ALS to determine display brightness settings based on a stored response curve for display brightness for a user. When the user overrides the response curve value for display brightness at a given ALS output, the display brightness setting based on the user input is used to modify the response curve for the ALS output to lesser extent than the user input. Over time the response curve will approach desired user settings for each value of the ALS output.